As data collection and dissemination technology continues to mature, there is an ever-widening chasm between data collection capabilities and data analysis and delivery capabilities. The result of ineffective data analysis is poor utilization of data resources and loss of opportunities to access useful data. For instance, a motorist, upon leaving work may want to know if the road home has ice on it. Alternatively, a motorist may desire information concerning traffic flow patterns along a specific stretch of roadway. Existing technology, available in many metropolitan areas, provides radio reports which include superficial data pertaining to traffic and generalized weather forecasts, Unfortunately, often the data provided is usually neither sufficiently detailed nor timely delivered. Part of this problem stems from the fact that there is simply too much data collected and too little time, and bandwidth, for complete dissemination, the result is poor utilization of data collection resources, and missed opportunities to provide data to potential users. Such missed opportunities, raise the price of data collection resources because fewer users must bear the costs associated with the data collection resources. Because of the nature of many types of situational-awareness data, the failure to provide timely delivery can mean that the data is essentially worthless, except for possible subsequent statistical evaluation.
Therefore it would be desirable to disseminate detailed data to users quickly, thereby allowing the users to consider the data and make better decisions, and lower the cost associated with data collection.